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Algorithmic recognition of the Verb

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Министерство образования Республики Беларусь Учреждение образования «Гомельский государственный университет им. Ф. Скорины» Филологический факультет Курсовая работа Algori hmic recog i io of he Verb Исполнитель: Студентка группы К-42 Марченко Т.Е. Гомель 2005 Co e I roduc io Basic assump io s a d some fac s 1 Algori hm for au oma ic recog i io of verbal a d omi al word groups 2 Lis s of markers used by Algori hm o 1 3 ex sample processed by he algori hm Examples of ha d checki g of he performa ce of he algori hm Co clusio Refere ces I roduc io he adve a d he subseque wide use of formal grammars for ex sy hesis a d for formal represe a io of he s ruc ure of he Se e ce could o produce adequa e resul s whe applied o ex a alysis. herefore a be er a d more sui able solu io was sough . Such a solu io was fou d i he algori hmic approach for he purposes of ex a alysis. he algori hmic approach uses series of i s ruc io s, wri e i a ural La guage a d orga ized i flow char s, wi h he aim of a alysi g cer ai aspec s of he gramma ical s ruc ure of he Se e ce. he procedures - i he form of a fi i e seque ce of i s ruc io s orga ized i a algori hm - are based o he gramma ical a d sy ac ical i forma io co ai ed i he Se e ce. he me hod used i his chap er closely follows he approach adop ed by he all-Russia group S a is ika Rechi i he 1970s a d described i a umber of publica io s (Kovcri , 1972: Mihailova, 1973; Georgiev, 1976). I is o be o ed, however, ha he resul s achieved by he algori hmic procedures described i his s udy by far exceed he resul s for he E glish la guage ob ai ed by Primov a d Soroki a (1970) usi g he same me hod. ( o preve u au horized commercial use he au hors published o ly he block-scheme of he algori hm.) Basic assump io s a d some fac s I is a well k ow fac ha ma y difficul ies are e cou ered i ex Processi g. A major difficul y, which if o removed firs would hamper a y fur her progress, is he ambigui y prese i he wordforms ha po e ially belo g o more ha o e Par of Speech whe ake ou of co ex . herefore i is esse ial o fi d he fea ures ha disambigua e he wordforms whe used i a co ex a d o defi e he disambigua io process algori hmically. As a firs s ep i his direc io we have chose o disambigua e hose wordforms which po e ially (whe ou of co ex , i a dic io ary) ca be a ribu ed o more ha o e Par of Speech a d where o e of he possibili ies is a Verb. hese possibili ies i clude Verb or ou (as i s ay), Verb or ou or Adjec ive (as i pai , crash), Verb or Adjec ive (as i calm), Verb or Par iciple (as i se led, asked, pu ), Verb or ou or Par iciple (as i ru , abode, bid), Verb or Adjec ive or Par iciple (as i closed), a d Verb or ou or Par iciple or Adjec ive (as i cu ). We'll s ar wi h he assump io ha for every wordform i he Se e ce here are o ly wo possibili ies: o be or o o be a Verb. herefore, o ly provisio ally, exclusively for he purposes of he prese ype of descrip io a d subseque algori hmic a alysis of he Se e ce, we shall assume ha all wordforms i he Se e ce which are o Verbs belo g o he o -verbal or omi al Word Group ( G).

As a resul of his defi i io , he G will i corpora e he ou , he Adjec ive, he Adverb, he umeral, he Pro ou , he Preposi io a d he Par iciple 1s used as a a ribu e (as i he bes selec ed audie ce) or as a Compleme (as i we'll regard his ma er se led). All he wordforms i he Se e ce which are Verbs form he Verbal Group (VG). he VG i cludes all mai a d Auxiliary Verbs, he Par icle o (used wi h he I fi i ive of he Verb), all verbal phrases co sis i g of a Verb a d a ou (such as ake place, ake par , e c.) or a Verb a d a Adverb (such as go ou , ge up, se aside, e c.), a d he Par iciple 2 d used i he compou d Verbal e ses (such as had arrived). he formal fea ures which help us recog ize he omi al or verbal charac er of a wordform are called 'markers' (Ses ier a d Dupuis, 1962). Some markers, such as he, a, a , a , by, o , i , e c. (mos of hem are Preposi io s), predic wi h 100 per ce accuracy he omi al a ure of he wordform immedia ely followi g hem (so lo g as he Preposi io s are o par of a phrasal Verb). O her markers, i cludi g wordform e di gs such as -i g a d -es, or a Preposi io which is also a Par icle such as o, e c., whe used si gly o heir ow (wi hou he help of o her markers) ca o predic accura ely he verbal or omi al charac er of a wordform. Co sideri g he fac ha o all markers give 100 per ce predic abili y (eve whe all markers i he immedia e vici i y of a wordform are ake i o co sidera io ), i becomes evide ha he e ire process of formal ex a alysis usi g his me hod is based, o a cer ai degree, o probabili y. he ques io is how o reduce he possible errors. o his purpose, he followi g procedures were used: a) he co ex of a wordform was explored for markers, movi g back a d for h up o hree words o he lef a d o he righ of he wordform; b; some algori hmic i s ruc io s preceded o hers i seque ce as a ma er of rule i order o ac as a addi io al scree i g; o decisio was ake prema urely, wi hou sufficie gramma ical a d sy ac ical evide ce bei g co ai ed i he markers; o i s ruc io was co sidered o be fi al wi hou sufficie checki g a d es s provi g he success ra e of i s performa ce. he algori hm prese ed i Sec io 3 below, umbered as Algori hm o 1 i.Georgicv, 1991), whe es ed o ex s chose a ra dom, correc ly recog ized o average 98 words ou of every 100. he algori hm uses Lis s of markers. Algori hm for au oma ic recog i io of verbal a d omi al word groups he block-scheme of he algori hm is show i Figure 1.1. Recog i io of Auxiliary Words, Abbrevia io s, Pu c ua io Marks a d figures of up o 3-le er le g h !'prese ed i Lis s) Words over 3-le cr le g h: search firs lef , he righ (up o 3 words i each direc io ) for markers (prese ed i Lis s) u il e ough evide ce is ga hered for a correc a ribu io of he ru i g word Ou pu resul : a ribu io of he ru i g word o o e of he groups (verbal or omi al)Figure 1.1 Block-scheme of Algori hm o 1 o e: he algori hm. 302 digi al i s ruc io s i all, is available o he I er e (see I er e Dow loads a he e d of he book). 1 Lis s of markers used by Algori hm o 1 (i) Lis o 1: for, ei, wo, o e, may, fig, a y, day, she, his, him, her, you, me , i s, six, sex, e , low, fa , old, few, ew, ow, sea, ye , ago, or, all, per, era, ra , lo , our, way, leg, hay, key, ea, lee, oak, big, who, ub, pe , law, hu , gu , wi , ha , po , how, far, ca , dog, ray, ho , op, via, why, Mrs, .,

e c. (ii) Lis o 2: was, are, o , ge , go , bid, had, did, due, see, saw, li , le , say,me , ro . off, fix, lie, die, dye, lay, si , ry, led, i , . . ., e c. (iii) Lis o 3: pay, dip, be , age, ca , ma , oil, e d, fu , dry, log, use, se , air, ag, map, bar, mug, mud, ar, op, pad, raw, row, gas, red, rig, fi , ow , le , aid, ac , cu , ax, pu , ., e c. (iv) Lis o 4: o, all, hus, bo h, ma y, may, migh , whe , Perso al Pro ou s, so, mus , would, of e , did, make, made, if, ca , will, shall, ., e c. (v) Lis o 5: whe , he, a, a , is, o, be, are, ha , which, was, some, o, will, ca , were, have, may, ha , has, bei g, made, where, mus , o her, such, would, each, he , should, here, hose, could, well, eve , propor io al, par icular(ly), havi g, ca o , ca ' , shall, la er, migh , ow, of e , had, almos , ca o , of, i , for, wi h, by, his, from, a , o , if, be wee , i o, hrough, per, over, above, because, u der, below, while, before, co cer i g, as, o e, ., e c. (vi) Lis o 6: wi h, his, ha , from, which, hese, hose, ha , he , where, whe , also, more, i o, o her, o ly, same, some, here, such, abou , leas , hem, early, ei her, while, mos , hus, each, u der, heir, hey, af er, less, ear, above, hree, bo h, several, below, firs , much, ma y, zero, eve , he ce, before, qui e, ra her, ill, u il, bes , dow , over, above, hrough, Reflexive Pro ou s, self, whe her, o o, o ce, si ce, oward (s), already, every, elsewhere, hi g, o hi g, always, perhaps, some imes, a y hi g, some hi g, every hi g, o herwise, of e , las , arou d, s ill, i s ead, foreword, la er, jus , behi d, ., e c. (vii)Lis o 7: I cludes all Irregular Verbs, wi h he followi g wordforms: Prese , Prese 3rd perso si gular, Pas a d Pas Par iciple. (viii)Lis o 8: - ed, -ded, -ied, - ed, -red, -sed, -ked, -wed, -bed, -hed, -ped -led, -ved, -reed, -ced, -med, -zed, -yed, -ued, ., e c.(ix)Lis o 9: -ous, -i y, -less, -ph, -'s (excep i i 's, wha 's, ha 's, here's, e c.), - ess, -e ce, -ic, -ее, -ly, -is, -al, - y, -que, -( )er, -( )or, - h (excep i wor h), -ul8, -me , -sio (s), ., e c. (x)Lis o 10: Comprises a full lis of all umerals (Cardi al a d Ordi al). 2 ex sample processed by he algori hm ex Word Group She G oddedVG Agai a d G Pa edVG My arm, a small familiar ges ure which always G Ma aged o co veyVG Bo h u ders a di g a d dismissal. G 3 Examples of ha d checki g of he performa ce of he algori hm Le us see how he followi g se e ce will be processed by Algori hm o 1, word by word: Her apar me was o a floor by i self a he op of wha had o ce bee a si gle dwelli g, bu which lo g ago was divided i o separa ely re ed livi g quar ers. Firs he algori hm picks up he firs word of he se e ce (of he ex ), i our case his is he word her, wi h i s ruc io o 1. he same i s ruc io always ascer ai s ha he ex has o e ded ye . he he algori hm proceeds o a alyse he word her by aski g ques io s abou i a d verifyi g he a swers o hose ques io s by compari g he word her wi h lis s of o her words a d Pu c ua io Marks, hus es ablishi g, gradually, ha he word her is o a Pu c ua io Mark ('opera io s 3-5), ha i is o a figure ( umber) ci her (opera io 5 7i, a d ha i s le g h exceeds wo le ers (opera io 8).

TheyБЂ™d been used for all kinds of stuff in Macsyma, and Macsyma users were using them all the time. And yet a report came in from Bill Gosper saying, БЂњThe quotient of these two integers is wrong.БЂ« And he could tell because the quotient was supposed to be very near a decimal multiple of pi. These were numbers about a hundred digits each and it really wasnБЂ™t feasible to trace through the entire thing by hand because the division routine was fairly complicated and these were big numbers. So I stared at the code and didnБЂ™t see anything obviously wrong. But one thing that caught my eye was a conditional step that I didnБЂ™t quite understand. These routines were based on algorithms from Knuth, so I got Knuth off the shelf and read the specification and proceeded to map the steps of KnuthБЂ™s algorithm onto the assembly-language code. And what caught my eye in Knuth was a comment that this step happens rarelyБЂ”with a probability of roughly only one in two to the size of the word. So we expected this to happen only once in every four billion times or something

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2. Comparison of the Renaissance and Enlightenment (Сравнение Ренессанса и Просвещения)

3. Going public and the dividend policy of the company

4. My modern image of the United States

5. The fastest computers of the world

6. Представители Ренессанса (Representatives of the renaissance and thair contribution to the literature)
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16. Discovery of the New World

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18. Model of the nucleus of atom and the table of elements

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20. The pride of the nation

21. Eyck, Jan van: The Adoration of the Lamb

22. The main fault of The 70s or the years of “might-have-been hopes”
23. Dawn of the digital information era
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36. Famous journalist of the English-speaking country. Benjamin Franklin

37. Relief of the UK

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60. The impact of the French Revolution on the European system

61. The Radicalism of the American Revolution

62. The Algorithm of a Start and the Development of International Conflicts and Possible Ways of Their Solution

63. The effect of light intensity on the amount of chlorophyll in “Cicer arietinum”

64. The Economy of Great Britain

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68. On the problem of crystal metallic lattice in the densest packings of chemical elements

69. Alaska’s Wildlife: on the Verge of Extinction (Живая природа Штата Аляска на грани исчезновения)

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72. The Tower of London
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78. The history of Old English and its development

79. Consequence of building the National Missile Defense

80. The house of my dream

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82. Ways of exploring the world

83. The Influence of English Mass Culture on Estonia

84. Династия Тюдоров (essay the house of Tudor)

85. Evaluating the GPRS Radio Interface for Different Quality of Service Profiles

86. Allergy Enlightenment: Is Modern Medicine Fighting the Causes of This Ailment? Are Doctors Telling Us Everything?
87. Преп. Максим исповедник о бесконечности человека (st. Maximus the confessor on the infinity of man)
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89. Redesigning the Dragon Financial Reform in the Peoples Republic of China

90. The history of England can be defined as the gradual process of Parliament asserting its authority over the monarchy

91. The practice of modern medicine


93. The City of London and its role as a financial centre

94. The United States of America

95. London - the capital of Great Britain

96. The History of Moscow

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97. Review of Bill Gates’s book ”Business @ the Speed of Thought”

98. The United States Of America

99. The faculty of mathematics of Irkutsk State University

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